Optical neural network can process information in parallel by using the technology based on free-space and integrated platform. Over the last half century, the development of integrated circuits has been limited by Moore’s law. We know that neural network is based on the digital computer for successive calculation, most of which cannot be made into real-time processing system. Therefore, it is necessary to develop ONN for real-time processing and device miniaturization. In this paper, we review the progress of optical neural networks. Firstly, based on the principle of artificial neural networks, we elaborate the essence of optical matrix multiplier for linear operation. Then we introduce the optical neural network achieved by free-space optical interconnection and waveguide optical interconnection. Finally we talk about the nonlinearity in optical neural networks. With the gradual maturity of nanotechnology and the rapid advancement of silicon photonic integrated circuits, the progress of integrated photonic neural network has been promoted. Therefore, the construction of optical neural network on the future integrated photonic platform has potential application value.

In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification due to their exceptional performance in local feature extraction. However, due to the local join and weight sharing properties of the convolution kernel, CNNs have limitations in long-distance modeling, and deeper networks tend to increase computational costs. To address these issues, this paper proposes a vision Transformer (VIT) based on the light self-Gaussian-attention (LSGA) mechanism, which extracts global deep semantic features. Firstly, the hybrid spatial-spectral Tokenizer module extracts shallow spatial-spectral features and expands image patches to generate Tokens. Next, the light self-attention uses Q (Query), X (Origin input), and X instead of Q, K (Key), and V (Value) to reduce the computation and parameters. Furthermore, to avoid the lack of location information resulting in the aliasing of central and neighborhood features, we devise Gaussian absolute position bias to simulate HSI data distribution and make the attention weight closer to the central query block. Several experiments verify the effectiveness of the proposed method, which outperforms state-of-the-art methods on four datasets. Specifically, we observed a 0.62% accuracy improvement over A2S2K and a 0.11% improvement over SSFTT. In conclusion, the proposed LSGA-VIT method demonstrates promising results in HSI classification and shows potential in addressing the issues of location-aware long-distance modeling and computational cost. Our codes are available at https://github.com/machao132/LSGA-VIT.

In this paper, we propose a deep learning method for single infrared image optical noise removal. With a fully convolutional neural network, it is able to eliminate the optical noise in a single infrared image. Our architecture consists of two networks: a denoising network and a conditional discriminator. The denoising network takes a noise image as input and outputs a denoising result, while the discriminator tries to make the output look more like the target. Actually, only in the testing phase, this method is feed-forward. Significant image quality in experiments is achieved compared with the existing method. Code is available at https://github.com/Kuangxd/ONR.

Infrared image enhancement is a crucial pre-processing technique in intelligent urban surveillance systems for Smart City applications. Existing grayscale mapping-based algorithms always suffer from over-enhancement of the background, noise amplification, and brightness distortion. To cope with these problems, an infrared image enhancement method based on adaptive histogram partition and brightness correction is proposed. First, the grayscale histogram is adaptively segmented into several sub-histograms by a locally weighted scatter plot smoothing algorithm and local minima examination. Then, the fore-and background sub-histograms are distinguished according to a proposed metric called grayscale density. The foreground sub-histograms are equalized using a local contrast weighted distribution for the purpose of enhancing the local details, while the background sub-histograms maintain the corresponding proportions of the whole dynamic range in order to avoid over-enhancement. Meanwhile, a visual correction factor considering the property of human vision is designed to reduce the effect of noise during the procedure of grayscale re-mapping. Lastly, particle swarm optimization is used to correct the mean brightness of the output by virtue of a reference image. Both qualitative and quantitative evaluations implemented on real infrared images demonstrate the superiority of our method when compared with other conventional methods.

Realizing robust infrared small target detection in complex backgrounds is of great essence for infrared search and tracking (IRST) applications. However, the high-intensity structures in background regions, such as the sharp edges, make it a challenging task, especially when the target is with low signal-to-clutter ratio (SCR). To address this issue, we propose an infrared small target detection method using local contrast-weighted multidirectional derivative (LCWMD). It is a robust detector that comprehensively considers the target property, background information, and the relation between them. First, we take into account the approximate isotropy of the infrared small target and present a new multidirectional derivative with penalty factors based on the Facet model to develop the target salience in the local region. Second, a dual local contrast fusion model with the tri-layer design is introduced to amplify the difference between the target and the background, so as to further suppress the high-intensity structural clutters. Finally, the LCWMD map is obtained by weighting the above two filtered maps, after which an adaptive segmentation operation is applied to accomplish the target detection. The results of comparative experiments implemented on real infrared images demonstrate that our method outperforms other state-of-the-art detectors by several times in terms of signal-to-clutter ratio gain (SCRG) and background suppression factor (BSF).

In this paper, an adaptive contrast enhancement method based on the neighborhood conditional histogram is proposed to improve the visual quality of thermal infrared images. Existing block-based local contrast enhancement methods usually suffer from the over-enhancement of smooth regions or the loss of some details. To address these drawbacks, we first introduce a neighborhood conditional histogram to adaptively enhance the contrast and avoid the over-enhancement caused by the original histogram. Then the clip-redistributed histogram of the contrast-limited adaptive histogram equalization (CLAHE) is replaced by the neighborhood conditional histogram. In addition, the local mapping function of each sub-block is updated based on the global mapping function to further eliminate the block artifacts. Lastly, the optimized local contrast enhancement process, which combines both global and local enhanced results is employed to obtain the desired enhanced result. Experiments are conducted to evaluate the performance of the proposed method and the other five methods are introduced as a comparison. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the other block-based methods on local contrast enhancement, visual quality improvement, and noise suppression.